Silvia Paddock

The FKBP5-Gene in Depression and Treatment Response—an Association Study in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Cohort

BACKGROUND In a recent study of several antidepressant drugs in hospitalized, non-Hispanic White patients, Binder et al. reported association of markers located within the FKBP5 gene with treatment response after 2 and 5 weeks. Individuals homozygous for the TT-genotype at one of the markers (rs1360780) reported more depressive episodes and responded better to antidepressant treatment. There was no association between markers in FKBP5 and disease. The present study aimed at studying the associated FKBP5 markers in the ethnically diverse Sequenced Treatment Alternatives to Relieve Depression (STAR*D) sample of non-hospitalized patients treated with citalopram. METHODS We used clinical data and DNA samples from 1809 outpatients with non-psychotic major depressive disorder (DSM-IV criteria), who received up to 14 weeks of citalopram. A subset of 1523 patients of White non-Hispanic or Black race was matched with 739 control subjects for a case-control analysis. The markers rs1360780 and rs4713916 were genotyped on the Illumina platform. TaqMan-assay was used for marker rs3800373. RESULTS In the case-control analysis, marker rs1360780 was significantly associated with disease status in the White non-Hispanic sample after correction for multiple testing. A significant association was also found between rs4713916 and remission. Markers rs1360780 and rs4713916 were in strong linkage disequilibrium in the White non-Hispanic but not in the Black population. There was no significant difference in the number of previous episodes of depression between genotypes at any of the three markers. CONCLUSIONS These results indicate that FKBP5 is an important target for further studies of depression and treatment response.

MAGI1 copy number variation in bipolar affective disorder and schizophrenia.

BACKGROUND Bipolar affective disorder (BPAD) and schizophrenia (SZ) are devastating psychiatric disorders that each affect about 1% of the population worldwide. Identification of new drug targets is an important step toward better treatment of these poorly understood diseases. METHODS Genome-wide copy number variation (CNV) was assessed and variants were ranked by co-occurrence with disease in 48 BPAD families. Additional support for involvement of the highest-ranking CNV from the family-based analysis in psychiatric disease was obtained through analysis of 4084 samples with BPAD, SZ, or schizoaffective disorder. Finally, a pooled analysis of in-house and published datasets was carried out including 10,925 cases with BPAD, SZ, or schizoaffective disorder and 16,747 controls. RESULTS In the family-based analysis, an approximately 200 kilobase (kb) deletion in the first intron of the MAGI1 gene was identified that segregated with BPAD in a pedigree (six out of six affected individuals; parametric logarithm of the odds score = 1.14). In the pooled analysis, seven additional insertions or deletions over 100 kb were identified in MAGI1 in cases, while only two such CNV events were identified in the same gene in controls (p = .023; Fishers exact test). Because earlier work had identified a CNV in the close relative MAGI2 in SZ, the study was extended to include MAGI2. In the pooled analysis of MAGI2, two large deletions were found in cases, and two duplications were detected in controls. CONCLUSIONS Results presented herein provide further evidence for a role of MAGI1 and MAGI2 in BPAD and SZ etiology.

Pharmacogenetics of major depression: insights from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial

Major depression is a serious mental illness frequently associated with devastating consequences for those affected. Suicide rates are significantly elevated, creating a sense of urgency to identify effective yet safe treatment options. A plethora of antidepressants are available on the market today, designed to act on different neurotransmitter systems in the brain, providing the clinician with several treatment strategies. There is, however, very little guidance as to which antidepressant may be most successful in a certain individual. Biomarkers that can predict treatment outcome would thus be of great value, shortening the time until remission and reducing costs for the healthcare system by reducing unsuccessful treatment attempts. The proven contribution of heredity to major depression risk suggests that genetic markers may be good biomarkers for treatment outcome.The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study and a large ancillary pharmacogenetic study in 1953 STAR*D participants constitute the largest effort to date to identify genetic predictors of antidepressant treatment outcome. In this review, the results of candidate gene studies carried out so far are summarized and discussed, and some future directions are proposed.

Association of a protective paraoxonase 1 (PON1) polymorphism in Parkinson's disease

Pesticide exposure has been suggested to increase the risk to develop Parkinsons disease (PD). The arylesterase paraoxonase 1 (PON1) is mainly expressed in the liver and hydrolyzes organophosphates such as pesticides. The polymorphism Leu54Met (rs854560) in PON1, impairing enzyme activity and leading to decreased PON1 expression levels, has been reported to be associated with Parkinsons disease (PD). PON1 is part of a cluster on chromosome 7q21.3 together with PON2 and PON3. We investigated the occurrence of four additional polymorphisms in PON1 and two in PON2 in a Swedish PD case-control material. We found a significant association (p=0.007) with a PON1 promoter polymorphism, rs854571. The minor allele was more common among controls than PD cases which suggest a protective effect. This is strengthened by the fact that rs854571 is in strong linkage disequilibrium with another PON1 promoter polymorphism, rs854572, reported to increase PON1 gene expression. Our findings support the hypothesis that PON1 is involved in the etiology of PD and that higher PON1 levels are reducing the risk for PD.

Genetic variation in GRIK4 and the implications for antidepressant treatment

Clinical depression is one of the leading causes of long-term disability and suffering worldwide [1]. owing to significantly elevated suicide rates in the affected population, it is also frequently a fatal condition [2]. To make things even worse, a diagnosis of depression increases the risk for many other kinds of morbidity and mortality [3], causing a large burden for public-health systems around the globe. Since the 1950s, there have been partial successes in the pharmacological treatment of depression, mainly by means of drugs that increase neurotransmitter concentrations in the synaptic cleft. The discoveries of the first antidepressants were often serendipitous, and many of the initial drugs were originally developed to treat other diseases. During the 1970s, researchers began to use insights from the treatment with these early drugs in order to develop a new class of antidepressants that selectively inhibits the transporter of the neurotransmitter serotonin (selective serotonin-reuptake inhibitors [SSRIs]). The first SSRI approved by the US FDA was fluoxetine, sold as Prozac®. Fluoxetine quickly became the most widely used pharmaceutical treatment option for depression and was soon celebrated as the new ‘blockbuster’ drug in psychiatry. Supported by the successes of SSRIs in the clinic, the serotonergic system also became a primary target for research into the causes of the disease itself. If depression can be treated using a drug that was designed to act on the serotonergic system, then we should look for the defect underlying the disease in that same system. But is it really that simple? At least two alternative explanations suggest themselves:

Significant Association of Estrogen Receptor Binding Site Variation with Bipolar Disorder in Females

Major depression is nearly twice as prevalent in women compared to men. In bipolar disorder, depressive episodes have been reported to be more common amongst female patients. Furthermore, periods of depression often correlate with periods of hormonal fluctuations. A link between hormone signaling and these mood disorders has, therefore, been suggested to exist in many studies. Estrogen, one of the primary female sex hormones, mediates its effect mostly by binding to estrogen receptors (ERs). Nuclear ERs function as transcription factors and regulate gene transcription by binding to specific DNA sequences. A nucleotide change in the binding sequence might alter the binding efficiency, which could affect transcription levels of nearby genes. In order to investigate if variation in ER DNA-binding sequences may be involved in mood disorders, we conducted a genome-wide study of ER DNA-binding in patients diagnosed with major depression or bipolar disorder. Association studies were performed within each gender separately and the results were corrected for multiple testing by the Bonferroni method. In the female bipolar disorder material a significant association result was found for rs6023059 (corrected p-value = 0.023; odds ratio (OR) 0.681, 95% confidence interval (CI) 0.570–0.814), a single nucleotide polymorphism (SNP) placed downstream of the gene coding for transglutaminase 2 (TGM2). Thus, females with a specific genotype at this SNP may be more vulnerable to fluctuating estrogen levels, which may then act as a triggering factor for bipolar disorder.

PACE Continuous Innovation Indicators-a novel tool to measure progress in cancer treatments.

Concerns about rising health care costs and the often incremental nature of improvements in health outcomes continue to fuel intense debates about ‘progress’ and ‘value’ in cancer research. In times of tightening fiscal constraints, it is increasingly important for patients and their representatives to define what constitutes ’value’ to them. It is clear that diverse stakeholders have different priorities. Harmonisation of values may be neither possible nor desirable. Stakeholders lack tools to visualise or otherwise express these differences and to track progress in cancer treatments based on variable sets of values. The Patient Access to Cancer care Excellence (PACE) Continuous Innovation Indicators are novel, scientifically rigorous progress trackers that employ a three-step process to quantify progress in cancer treatments: 1) mine the literature to determine the strength of the evidence supporting each treatment; 2) allow users to weight the analysis according to their priorities and values; and 3) calculate Evidence Scores (E-Scores), a novel measure to track progress, based on the strength of the evidence weighted by the assigned value. We herein introduce a novel, flexible value model, show how the values from the model can be used to weight the evidence from the scientific literature to obtain E-Scores, and illustrate how assigning different values to new treatments influences the E-Scores. The Indicators allow users to learn how differing values lead to differing assessments of progress in cancer research and to check whether current incentives for innovation are aligned with their value model. By comparing E-Scores generated by this tool, users are able to visualise the relative pace of innovation across areas of cancer research and how stepwise innovation can contribute to substantial progress against cancer over time. Learning from experience and mapping current unmet needs will help to support a broad audience of stakeholders in their efforts to accelerate and maximise progress against cancer.

ReMo-SNPs: a new software tool for identification of polymorphisms in regions and motifs genome-wide.

Summary Studies of complex genetic diseases have revealed many risk factors of small effect, but the combined amount of heritability explained is still low. Genome-wide association studies are often underpowered to identify true effects because of the very large number of parallel tests. There is, therefore, a great need to generate data sets that are enriched for those markers that have an increased a priori chance of being functional, such as markers in genomic regions involved in gene regulation. ReMo-SNPs is a computational program developed to aid researchers in the process of selecting functional SNPs for association analyses in user-specified regions and/or motifs genome-wide. The useful feature of automatic selection of genotyped markers in the user-provided material makes the output data ready to be used in a following association study. In this article we describe the program and its functions. We also validate the program by including an example study on three different transcription factors and results from an association study on two psychiatric phenotypes. The flexibility of the ReMo-SNPs program enables the user to study any region or sequence of interest, without limitation to transcription factor binding regions and motifs. The program is freely available at: http://www.neuro.ki.se/ReMo-SNPs/